{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# -- 将数据框命名为drinks\n",
    "# -- 哪个大陆(continent)平均消耗的啤酒(beer)更多？\n",
    "# -- 打印出每个大陆(continent)的红酒消耗(wine_servings)的描述性统计值\n",
    "# -- 打印出每个大陆每种酒类别的消耗平均值\n",
    "# -- 打印出每个大陆每种酒类别的消耗中位数\n",
    "# -- 打印出每个大陆对spirit饮品消耗的平均值，最大值和最小值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#将数据框命名为drinks\n",
    "drinks = pd.read_csv('data/drinks.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
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       "      <th>EU</th>\n",
       "      <td>193.777778</td>\n",
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       "           beer_servings\n",
       "continent               \n",
       "EU            193.777778"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#哪个大陆(continent)平均消耗的啤酒(beer)更多？\n",
    "drinks[['beer_servings','continent']].groupby('continent').mean().sort_values('beer_servings',ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "      <th>continent</th>\n",
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       "      <th></th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.00</td>\n",
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       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>45.0</td>\n",
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       "      <td>97.421738</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>195.00</td>\n",
       "      <td>370.0</td>\n",
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       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>16.0</td>\n",
       "      <td>35.625000</td>\n",
       "      <td>64.555790</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>23.25</td>\n",
       "      <td>212.0</td>\n",
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       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>12.0</td>\n",
       "      <td>62.416667</td>\n",
       "      <td>88.620189</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>98.50</td>\n",
       "      <td>221.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "           count        mean        std  min   25%    50%     75%    max\n",
       "continent                                                               \n",
       "AF          53.0   16.264151  38.846419  0.0   1.0    2.0   13.00  233.0\n",
       "AS          44.0    9.068182  21.667034  0.0   0.0    1.0    8.00  123.0\n",
       "EU          45.0  142.222222  97.421738  0.0  59.0  128.0  195.00  370.0\n",
       "OC          16.0   35.625000  64.555790  0.0   1.0    8.5   23.25  212.0\n",
       "SA          12.0   62.416667  88.620189  1.0   3.0   12.0   98.50  221.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- 打印出每个大陆(continent)的红酒消耗(wine_servings)的描述性统计值\n",
    "# drinks[['wine_servings','continent']].groupby('continent').sum()\n",
    "drinks.groupby('continent').wine_servings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
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       "      <th>continent</th>\n",
       "      <th></th>\n",
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       "      <td>61.471698</td>\n",
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       "      <td>3.007547</td>\n",
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       "      <th>AS</th>\n",
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       "      <td>9.068182</td>\n",
       "      <td>2.170455</td>\n",
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       "      <th>OC</th>\n",
       "      <td>89.687500</td>\n",
       "      <td>58.437500</td>\n",
       "      <td>35.625000</td>\n",
       "      <td>3.381250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>175.083333</td>\n",
       "      <td>114.750000</td>\n",
       "      <td>62.416667</td>\n",
       "      <td>6.308333</td>\n",
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      ],
      "text/plain": [
       "           beer_servings  spirit_servings  wine_servings  \\\n",
       "continent                                                  \n",
       "AF             61.471698        16.339623      16.264151   \n",
       "AS             37.045455        60.840909       9.068182   \n",
       "EU            193.777778       132.555556     142.222222   \n",
       "OC             89.687500        58.437500      35.625000   \n",
       "SA            175.083333       114.750000      62.416667   \n",
       "\n",
       "           total_litres_of_pure_alcohol  \n",
       "continent                                \n",
       "AF                             3.007547  \n",
       "AS                             2.170455  \n",
       "EU                             8.617778  \n",
       "OC                             3.381250  \n",
       "SA                             6.308333  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- 打印出每个大陆每种酒类别的消耗平均值\n",
    "# drinks\n",
    "# drinks[['beer_servings','spirit_servings','wine_servings','continent']].groupby('continent').mean()\n",
    "drinks.groupby('continent').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
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       "      <th>continent</th>\n",
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       "      <th>AF</th>\n",
       "      <td>32.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.30</td>\n",
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       "      <th>AS</th>\n",
       "      <td>17.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.20</td>\n",
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       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>219.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>10.00</td>\n",
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       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>52.5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>162.5</td>\n",
       "      <td>108.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.85</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "           beer_servings  spirit_servings  wine_servings  \\\n",
       "continent                                                  \n",
       "AF                  32.0              3.0            2.0   \n",
       "AS                  17.5             16.0            1.0   \n",
       "EU                 219.0            122.0          128.0   \n",
       "OC                  52.5             37.0            8.5   \n",
       "SA                 162.5            108.5           12.0   \n",
       "\n",
       "           total_litres_of_pure_alcohol  \n",
       "continent                                \n",
       "AF                                 2.30  \n",
       "AS                                 1.20  \n",
       "EU                                10.00  \n",
       "OC                                 1.75  \n",
       "SA                                 6.85  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- 打印出每个大陆每种酒类别的消耗中位数\n",
    "drinks.groupby('continent').median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "    <tr>\n",
       "      <th>continent</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>53.0</td>\n",
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       "      <th>AS</th>\n",
       "      <td>44.0</td>\n",
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       "      <td>84.362160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98.00</td>\n",
       "      <td>326.0</td>\n",
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       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>45.0</td>\n",
       "      <td>132.555556</td>\n",
       "      <td>77.589115</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.00</td>\n",
       "      <td>122.0</td>\n",
       "      <td>173.00</td>\n",
       "      <td>373.0</td>\n",
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       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>16.0</td>\n",
       "      <td>58.437500</td>\n",
       "      <td>70.504817</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>65.25</td>\n",
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       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>12.0</td>\n",
       "      <td>114.750000</td>\n",
       "      <td>77.077440</td>\n",
       "      <td>25.0</td>\n",
       "      <td>65.75</td>\n",
       "      <td>108.5</td>\n",
       "      <td>148.75</td>\n",
       "      <td>302.0</td>\n",
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      ],
      "text/plain": [
       "           count        mean        std   min    25%    50%     75%    max\n",
       "continent                                                                 \n",
       "AF          53.0   16.339623  28.102794   0.0   1.00    3.0   19.00  152.0\n",
       "AS          44.0   60.840909  84.362160   0.0   1.00   16.0   98.00  326.0\n",
       "EU          45.0  132.555556  77.589115   0.0  81.00  122.0  173.00  373.0\n",
       "OC          16.0   58.437500  70.504817   0.0  18.00   37.0   65.25  254.0\n",
       "SA          12.0  114.750000  77.077440  25.0  65.75  108.5  148.75  302.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#打印出每个大陆对spirit饮品消耗的平均值，最大值和最小值\n",
    "drinks.groupby('continent').spirit_servings.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
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